Improving Channel Charting with Representation-Constrained Autoencoders
OPEN ACCESS
Loading...
Author / Producer
Date
2019-07
Publication Type
Conference Paper
ETH Bibliography
no
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI). CC relies on dimensionality reduction of high-dimensional CSI features in order to construct a channel chart that captures spatial and radio geometries so that UEs close in space are close in the channel chart. In this paper, we demonstrate that autoencoder (AE)-based CC can be augmented with side information that is obtained during the CSI acquisition process. More specifically, we propose to include pairwise representation constraints into AEs with the goal of improving the quality of the learned channel charts. We show that such representation-constrained AEs recover the global geometry of the learned channel charts, which enables CC to perform approximate positioning without global navigation satellite systems or supervised learning methods that rely on extensive and expensive measurement campaigns.
Permanent link
Publication status
published
External links
Editor
Book title
2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Journal / series
Volume
Pages / Article No.
1 - 5
Publisher
IEEE
Event
20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2019)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09695 - Studer, Christoph / Studer, Christoph